Towards New Metrics for Urban Road Networks: Some Preliminary Evidence from Agent-Based Simulations

  • Arnaud BanosEmail author
  • Cyrille Genre-Grandpierre


Road networks are complex entities, which are arranged hierarchically both in their structure (topology) and by speed. This property has a strong influence on their performance, both at an individual and collective level. Indeed, they intrinsically favour car use, especially for distant trips. In that sense, they may contribute actively to urban sprawl, a non desirable property of urban growth. In this chapter, we propose and explore a strategy aimed at regulating and even reversing such a “speed metric”. Using agents, we simulate road traffic on various road network structures and show how limited but well targeted actions can have a strong global impact on the system.


Road Network Urban Sprawl Traffic Light Land Minis Distance Trip 
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  1. Banos, A., Godara, A., & Lassarre, S. (2005). Simulating pedestrians and cars behaviours in a virtual city: An agent-based approach. In Proceedings of the European Conference on Complex Systems, Paris, 14–18 Nov 2005.Google Scholar
  2. Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40, 35–41.CrossRefGoogle Scholar
  3. Genre-Grandpierre, C. (2007). Des réseaux lents contre la dépendance automobile? Concept et implications en milieu urbain. L’Espace Géographique, 1, 27–39.Google Scholar
  4. Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of National Academic Science USA, 99, 7821–7826.CrossRefGoogle Scholar
  5. Gutiérrez, J., Monzòn, A., & Pinero, J. M. (1998). Accessibility, network efficiency and transport infrastructure planning. Environment and Planning A, 30, 1337–1350.CrossRefGoogle Scholar
  6. Lämmer, S., & Helbing, D. (2008). Self-control of traffic lights and vehicle flows in urban road networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(4), P04019.CrossRefGoogle Scholar
  7. Levinson, D., & Kumar, A. (1994). The rational locator: Why travel times have remained stable. Journal of the American Planning Association, 60(3), 319–332.CrossRefGoogle Scholar
  8. Nagel, K., & Schreckenberg, M. (1992). Cellular automaton models for freeway traffic. Journal of Physics I, 2, 2221–2229.Google Scholar
  9. Newman, M. E. J. (2005). Power laws, Pareto distributions and Zipf’s law. Contemporary Physics, 46, 323–351.CrossRefGoogle Scholar
  10. Penn, A., Hillier, B., & Xu, J. (1998). Configurational modelling of urban movement networks. Environment and Planning B, 25, 59–84.CrossRefGoogle Scholar
  11. Stanley, H. E., & Ostrowsky, N. (1985). On growth and form: Fractal and non fractal patterns in physics. Nijhoff, Dordrecht.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Géographie-Cité, and ISC-PIF, CNRSParisFrance
  2. 2.ESPACEUniversité d’Avignon/CNRSAvignonFrance

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